Mixtures of Nonparametric Autoregression, revised

  • We consider data generating mechanisms which can be represented as mixtures of finitely many regression or autoregression models. We propose nonparametric estimators for the functions characterizing the various mixture components based on a local quasi maximum likelihood approach and prove their consistency. We present an EM algorithm for calculating the estimates numerically which is mainly based on iteratively applying common local smoothers and discuss its convergence properties.

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Author:Jürgen Franke, Jean-Pierre Stockis, Joseph Tadjuidje, W.K. Li
Serie (Series number):Report in Wirtschaftsmathematik (WIMA Report) (121)
Document Type:Preprint
Language of publication:English
Year of Completion:2009
Year of Publication:2009
Publishing Institute:Technische Universität Kaiserslautern
Creating Corporation:Fachbereich Mathematik, University of Kaiserslautern
Date of the Publication (Server):2009/07/27
Tag:EM algorithm; hidden variables; mixture; nonparametric regression
Faculties / Organisational entities:Kaiserslautern - Fachbereich Mathematik
DDC-Cassification:5 Naturwissenschaften und Mathematik / 510 Mathematik
MSC-Classification (mathematics):62-XX STATISTICS / 62Gxx Nonparametric inference / 62G08 Nonparametric regression
Licence (German):Standard gemäß KLUEDO-Leitlinien vor dem 27.05.2011